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library(readr)
library(readxl)
library(pheatmap)
library(RColorBrewer)
library(limma)
library(XML)
library(genefilter)
library(cowplot)
library(vsn)
library(preprocessCore)
library(Rcpp)
library(rje)
library(xlsx)
library(geneplotter)
library(tidyverse)
library(ggbeeswarm)
library(tsne)
if (!require("devtools")) {
install.packages("devtools", dependencies = TRUE)
library(devtools)
}
install_github("raivokolde/pheatmap")Skipping install of 'pheatmap' from a github remote, the SHA1 (f4fe3002) has not changed since last install.
Use `force = TRUE` to force installationFirst step: load the data (Peptides table)
peptides_83 <- read_delim("/mnt/Data/Anders_group/u/annika/83samples/20180611_EPNfull_Annika_peptides.txt", "\t", escape_double = FALSE, trim_ws = TRUE)Parsed with column specification:
cols(
.default = col_integer(),
Sequence = col_character(),
`N-term cleavage window` = col_character(),
`C-term cleavage window` = col_character(),
`Amino acid before` = col_character(),
`First amino acid` = col_character(),
`Second amino acid` = col_character(),
`Second last amino acid` = col_character(),
`Last amino acid` = col_character(),
`Amino acid after` = col_character(),
Mass = col_double(),
Proteins = col_character(),
`Leading razor protein` = col_character(),
`Gene names` = col_character(),
`Protein names` = col_character(),
`Unique (Groups)` = col_character(),
`Unique (Proteins)` = col_character(),
Charges = col_character(),
PEP = col_double(),
Score = col_double(),
`Identification type 70915_QE1_TM_EPN_3EP11_1_T2` = col_character()
# ... with 205 more columns
)
See spec(...) for full column specifications.
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|========================================================| 100% 191 MBnumber of columns of result is not a multiple of vector length (arg 1)5294 parsing failures.
row # A tibble: 5 x 5 col row col expected actual file expected <int> <chr> <chr> <chr> <chr> actual 1 1010 Intensity 80413_QE1_TM_fullEPN_13EP2 an integer 32286… '/mn… file 2 1010 Intensity 80416_QE1_TM_fullEPN_13EP11 an integer 22194… '/mn… row 3 1010 Intensity 80416_QE1_TM_fullEPN_NCH2838 an integer 23133… '/mn… col 4 1010 Intensity R1-2_7EP5 an integer 25138… '/mn… expected 5 1010 Intensity R2_13EP2 an integer 44682… '/mn…
... ................. ... ...................................................................... ........ ...................................................................... ...... ...................................................................... .... ...................................................................... ... ...................................................................... ... ...................................................................... ........ ......................................................................
See problems(...) for more details.peptides_83 <- rownames_to_column( peptides_83 )
peptides_83[1:10,1:20]# A tibble: 10 x 20
rowname Sequence `N-term cleavag… `C-term cleavag… `Amino acid bef…
<chr> <chr> <chr> <chr> <chr>
1 1 AAAAAAA… LVSENAGRAAAAAAAA AGAGAGAKQTPADGEA R
2 2 AAAAAAA… HVPAKPPRAAAAAAAA ASPGDSARQKLEETSA R
3 3 AAAAAAA… ________________ AAGAAGGRGSGPGRRR M
4 4 AAAAAAA… KNKTKKTKAAAAAAAA TTAATAAQ________ K
5 5 AAAAAAA… RGGRDRGRAAAAAAAA AAAAAVSRRRKAEYPR R
6 6 AAAAAAA… APLLGARRAAAAAAAA AAGAFAGRRAACGAVL R
7 7 AAAAAAA… ________________ FSVEDPVRKVGGGGTA M
8 8 AAAAAAA… ________________ SVEDPVRKVGGGGTAG M
9 9 AAAAAAA… NHKLRVDKAAAAAAAL AAAALQAKSDEKAAVA K
10 10 AAAAAAG… ________________ GPVAQGLKEALVDTLT M
# ... with 15 more variables: `First amino acid` <chr>, `Second amino
# acid` <chr>, `Second last amino acid` <chr>, `Last amino
# acid` <chr>, `Amino acid after` <chr>, `A Count` <int>, `R
# Count` <int>, `N Count` <int>, `D Count` <int>, `C Count` <int>,
# `Q Count` <int>, `E Count` <int>, `G Count` <int>, `H
# Count` <int>, `I Count` <int>Prepare the data for working with it (83 samples)
pept83_corr_backup <- peptides_83 %>%
dplyr::select( starts_with("Intensity") ) %>%
dplyr::select( -contains("Library") ) %>%
rename_all( str_replace, "Intensity ", "" ) %>%
rename_all( str_replace, "_QE1_TM_fullEPN", "" ) %>%
rename_all( str_replace, "70915_QE1_TM_EPN_", "" ) %>%
rename_all( str_replace, "_1_", "_" ) %>%
rename_all( str_replace, "80416_", "" ) %>%
rename_all( str_replace, "80413_", "" ) %>%
dplyr::select( -contains("Intensity") )
pept83_corr_backup[pept83_corr_backup==0]=NA
pept83_corr_backup=as.matrix( pept83_corr_backup )
pept83_corr <- log2( pept83_corr_backup[ rowSums( is.na( pept83_corr_backup ) )!=ncol( pept83_corr_backup ), ] )
pept83_corr2 <- pept83_corr_backup[ rowSums( is.na( pept83_corr_backup ) )!=ncol( pept83_corr_backup ), ] tumor groups and associated samples
pfa = c( "7EP34", "4EP29", "9EP29", "4EP5", "7EP18", "3EP15", "3EP18", "3EP22", "3EP9", "3EP26", "3EP55", "3EP73", "3EP74", "4EP24", "9EP9", "9EP1", "9EP33", "7EP3", "7EP4", "7EP11", "11EP32", "7EP32", "7EP48", "7EP26" )
relpos = c( "4EP49", "3EP19", "4EP48", "3EP30", "4EP54", "7EP35", "4EP51", "3EP50", "3EP28", "3EP8", "9EP13", "4EP47", "3EP11", "3EP54", "3EP67", "3EP72", "4EP53", "4EP44", "7EP1", "7EP9" )
pfb = c( "3EP2", "3EP7", "3EP49", "4EP3", "4EP26", "4EP32", "4EP35", "9EP30", "7EP5", "7EP49", "7EP15", "7EP22")
mpe = c( "13EP1", "13EP2", "13EP3", "5EP21", "13EP4", "13EP5", "13EP6" )
pfse = c( "7EP19", "11EP7", "13EP8", "13EP9", "13EP12", "13EP14", "13EP18" )
spine = c( "NCH439A", "NCH678N", "NCH752I", "NCH1594", "NCH4384" )
spinese = c( "13EP13", "NCH2838", "NCH4121" )
stse = c( "3EP3", "3EP46", "3EP75", "13EP10", "13EP11" )Create model frame (17 samples)
mf_83 <- as.tibble( colnames( pept83_corr_backup ) ) %>%
rename( tag = value ) %>%
mutate( replicate = ifelse( str_detect( tag, "R2"), "2", "1" ) ) %>%
mutate( processing = ifelse( str_detect( tag, "T2"), "old", "new" ) ) %>%
mutate( techn_repl = ifelse( str_detect( tag, "R1-2"), "yes", "no" ) ) %>%
mutate( sample = str_remove( tag, "R2_" ) ) %>%
mutate( sample = str_remove( sample, "_T2" ) ) %>%
mutate( sample = str_remove( sample, "old_" ) ) %>%
mutate( sample = str_remove( sample, "R1-2_" ) ) %>%
mutate( tumor = ifelse( sample%in%relpos, "relpos", "0" ) ) %>%
mutate( tumor = ifelse( sample%in%pfa, "pfa", tumor ) ) %>%
mutate( tumor = ifelse( sample%in%pfb, "pfb", tumor ) ) %>%
mutate( tumor = ifelse( sample%in%mpe, "mpe", tumor ) ) %>%
mutate( tumor = ifelse( sample%in%pfse, "pfse", tumor ) ) %>%
mutate( tumor = ifelse( sample%in%spine, "spine", tumor ) ) %>%
mutate( tumor = ifelse( sample%in%spinese, "spinese", tumor ) ) %>%
mutate( tumor = ifelse( sample%in%stse, "stse", tumor ) )have a first look at the clustering of the new measured samples
brks <- ( seq(0, 1, length.out=50)[-1] )^0.7
n = mf_83$processing=="new" & mf_83$replicate=="1"
tumor <- as.matrix( mf_83[,6] )
rownames( tumor ) <- mf_83$tag
tumor <- as.data.frame( tumor )
pheatmap(
cor(pept83_corr[ n,n ], use = "p"), colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = TRUE, cluster_cols = TRUE, breaks=c(-rev(brks),0,brks), na_col = "grey" , annotation_col = tumor )
compare old and new measured 17 samples
samples17 = mf_83$sample[mf_83$processing=="old"]
processing <- as.matrix( mf_83[,3] )
rownames( processing ) <- mf_83$tag
processing <- as.data.frame( processing )
pheatmap(
cor(pept83_corr[ , mf_83$sample%in%samples17 ], use = "p"), colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = TRUE, cluster_cols = TRUE, breaks=c(-rev(brks),0,brks), na_col = "grey" , annotation_col = tumor, annotation_row = processing)
multidensity plots of 17 samples
multidensity( pept83_corr[,mf_83$sample%in%samples17], legend=FALSE, col=c("black", "blue")[ as.integer( factor( mf_83$processing[mf_83$sample%in%samples17] ) ) ] )
multidensity( pept83_corr[,mf_83$sample%in%samples17], legend=FALSE, col=c("black", "blue")[ as.integer( factor( mf_83$tumor[mf_83$sample%in%samples17] ) ) ] )
Boxplot of 17 samples
boxplot(pept83_corr[,mf_83$sample%in%samples17], legend=FALSE, col=c("black", "blue")[ as.integer( factor( mf_83$processing ) ) ] )
vsn normalization of 17 samples
peptides_83_narm <- peptides_83[rowSums( is.na( pept83_corr_backup ) )!=ncol( pept83_corr_backup ),]
pept83_vsn <- normalizeVSN( pept83_corr2 ) vsn2: 67073 x 111 matrix (1 stratum).
Please use 'meanSdPlot' to verify the fit.rownames( pept83_vsn ) <- unlist( peptides_83_narm[,1] )
multidensity( pept83_vsn[,mf_83$sample%in%samples17], legend=FALSE, col=c("black", "blue")[ as.integer( factor( mf_83$processing ) ) ] )
pheatmap(
cor( pept83_vsn[ , mf_83$sample%in%samples17 ], use = "p" ), colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = TRUE, cluster_cols = TRUE, breaks=c(-rev(brks),0,brks), na_col = "grey" , annotation_col = tumor, annotation_row = processing )
quantile normalization of 17 samples (on log2 scale and original data) -> log2 scale -> enhances differences between old and new processed samples
pept83_quant <- normalize.quantiles( pept83_corr2 )
colnames( pept83_quant ) <- mf_83$tag
pept83_quant_log <- log2( normalize.quantiles( pept83_corr2 ) )
colnames( pept83_quant_log ) <- mf_83$tag
rownames( pept83_quant_log ) <- unlist( peptides_83_narm[,1] )
multidensity( pept83_quant[,mf_83$sample%in%samples17], legend=FALSE, col=c("black", "blue")[ as.integer( factor( mf_83$processing ) ) ] )
pheatmap(
cor( pept83_quant[ , mf_83$sample%in%samples17 ], use = "p" ), colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = TRUE, cluster_cols = TRUE, breaks=c(-rev(brks),0,brks), na_col = "grey" , annotation_col = tumor, annotation_row = processing )
pheatmap(
cor( pept83_quant_log[ , mf_83$sample%in%samples17 ], use = "p" ), colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = TRUE, cluster_cols = TRUE, breaks=c(-rev(brks),0,brks), na_col = "grey" , annotation_col = tumor, annotation_row = processing )
Compare replicates and original samples
replicates <- mf_83$sample[mf_83$replicate=="2"]
multidensity( pept83_corr[,mf_83$sample%in%replicates], legend=FALSE, col=c("black", "blue")[ as.integer( factor( mf_83$replicate[mf_83$sample%in%replicates] ) ) ] )
repl <- as.matrix( mf_83[,2] )
rownames( repl ) <- mf_83$tag
repl <- as.data.frame( repl )
pheatmap(
cor( pept83_corr[ , mf_83$sample%in%replicates ], use = "p" ), colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = TRUE, cluster_cols = TRUE, breaks=c(-rev(brks),0,brks), na_col = "grey" , annotation_col = tumor, annotation_row = repl )
pheatmap(
cor( pept83_quant_log[ , mf_83$sample%in%replicates ], use = "p" ), colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = TRUE, cluster_cols = TRUE, breaks=c(-rev(brks),0,brks), na_col = "grey" , annotation_col = tumor, annotation_row = repl )
Missing values
r1 <- mf_83$replicate=="2"
r2 <- mf_83$replicate=="1"&mf_83$sample%in%replicates
misses <- as.data.frame(
c( pept83_corr[rowSums(is.na(pept83_corr[,r1]))!=ncol(pept83_corr[,r1]),r1] %>%
apply( 2, function(x) sum(is.na(x))),
pept83_corr[rowSums(is.na(pept83_corr[,r2]))!=ncol(pept83_corr[,r2]),r2] %>%
apply( 2, function(x) sum(is.na(x))) ) )
misses <- rownames_to_column(misses)
colnames(misses) <- c("tag","missing_values")
misses <- left_join(misses, mf_83[mf_83$sample%in%replicates,])Joining, by = "tag"ord83 <- order( misses$missing_values[misses$replicate=="2"] )
ggplot( misses, aes( x=sample, y=missing_values, col=replicate, group=replicate ) ) +
geom_point() +
geom_smooth( method="lm", se=FALSE ) +
scale_x_discrete( limits=misses$sample[misses$replicate=="2"][ord83] ) 
calculate number of rows containing only NA
sum(rowSums(is.na(pept83_corr_backup))==ncol(pept83_corr_backup))[1] 12950correlation heatmap of quantile normalized and raw data
pheatmap(
cor(pept83_quant_log[ n,n ], use = "p"), colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = TRUE, cluster_cols = TRUE, breaks=c(-rev(brks),0,brks), na_col = "grey" , annotation_col = tumor )
pheatmap(
cor(pept83_corr_backup[ n,n ], use = "p"), colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = TRUE, cluster_cols = TRUE, breaks=c(-rev(brks),0,brks), na_col = "grey" , annotation_col = tumor )
Create linear model
mm <- model.matrix( ~ processing + tumor , mf_83 )
fit <- lmFit( pept83_vsn, mm )Partial NA coefficients for 54690 probe(s)fit_quant <- lmFit( pept83_quant_log, mm )Partial NA coefficients for 54690 probe(s)coeff83 <- fit$coefficientsplot correlation and peptides that can be found in all samples (coefficients fit)
pheatmap(
cor( coeff83[ complete.cases(coeff83), 2:8], use = "p" ), colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = TRUE, cluster_cols = TRUE, breaks=c(-rev(brks),0,brks), na_col = "grey" )
pheatmap(
coeff83[ complete.cases(coeff83), 2:8], colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = TRUE, cluster_cols = TRUE, breaks=c(-rev(brks),0,brks), na_col = "grey" )
plot peptides with highest SD (coefficients fit)
sd=apply( coeff83[complete.cases(coeff83),2:8], 1, sd, na.rm = TRUE )
ord_sd = order(sd, decreasing = TRUE)
pheatmap(
coeff83[complete.cases(coeff83),][ord_sd[1:100], 2:8], colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = FALSE, cluster_cols = TRUE, na_col = "grey" )
a <- peptides_83_narm[ peptides_83_narm$`Leading razor protein` == "Q15149",1]
a <- as.integer( unlist( a ) )
topTable(eBayes(fit), coef = 3:8, number = Inf)[as.character(a),] tumorpfa tumorpfb tumorpfse tumorrelpos tumorspine
104 -0.583398108 -0.337096686 0.397973492 -1.145175664 NA
299 NA NA NA NA NA
300 0.875193016 0.505856247 2.104968381 0.362323438 -0.37362272
509 2.693909834 1.384092965 3.106938152 0.537080806 2.30937534
539 1.312090763 0.359331394 2.546576389 0.185706995 1.17861803
540 2.366211118 0.917780437 2.966022678 NA NA
1587 NA NA NA NA NA
1693 -1.162961181 -3.366918387 NA NA NA
1694 NA -0.109971430 1.922971656 NA NA
1900 1.734524278 1.088764338 3.060597213 -0.523059149 0.83920344
1956 1.017455072 0.567805161 1.786659701 -0.901447245 0.03160753
2170 -0.438155537 1.921455801 0.313751057 -0.098559334 NA
2171 1.773919617 0.076015285 2.803683872 -0.451813567 0.89897419
2212 0.745431772 -0.537999442 1.432460836 -1.773403229 -0.57108767
2213 0.006467345 -0.041840987 1.450703586 -0.318474045 NA
2245 0.870200617 -2.515316356 -0.902558285 0.119740319 NA
2618 -2.147159750 -2.193173527 -2.709920197 -1.604473617 -1.24448335
2619 2.033063178 0.688461932 2.605888417 -0.607761159 1.33754617
2800 2.512625754 1.272459301 3.108882830 -0.101071232 1.88962896
2928 1.900122673 0.823408266 0.475683597 -1.123592181 -1.56046248
2939 2.238136976 1.339213045 2.286394719 -0.266970133 1.65046516
2984 2.408196560 1.130318251 3.054376308 0.059195971 1.57136380
3007 0.946580144 0.604931134 1.263515807 -0.937622835 NA
3020 2.643631511 1.117201449 3.130361071 0.065748091 1.69131808
3565 NA NA NA NA NA
3583 0.708668356 -0.097580201 0.906050083 -1.266687959 -0.28848320
3678 2.122534316 0.671650729 2.433467366 -0.051080198 1.36398849
4547 1.639796226 0.280595651 1.918492233 -0.202713498 0.07368316
4548 -0.476873463 0.218429037 0.640782302 -1.687513107 0.16034621
5532 3.158078663 0.744794764 2.955643163 -0.455506178 1.64934046
5566 1.983350224 0.879673352 2.617608419 -0.347688192 1.35766622
5573 1.044378449 -1.550353820 1.404871557 0.057467405 NA
5575 1.827641023 0.331270161 2.638880783 -0.348467949 1.11271017
5576 0.064780386 -0.074574352 2.378443117 -0.353316240 NA
5587 1.593568054 0.433468871 1.123047109 NA -0.73616800
5693 2.203600101 NA 2.545101849 1.022205514 NA
5694 1.782545270 -0.040003931 2.251317185 -1.310913539 1.12662994
5695 0.505026005 0.073391520 1.660680346 -0.772909242 NA
5769 2.692645888 0.797739251 3.626851513 0.018009421 1.73560397
5770 0.715603157 0.225961250 1.161260529 -1.379014371 -0.77338783
5793 -0.402145164 -0.378583654 -0.075626436 -1.055554527 -0.16942863
5883 -0.718180039 0.208055141 -0.915427854 -0.719325077 -1.30091080
5891 -1.203485934 1.565062659 -0.467760555 NA NA
5956 1.260724409 -0.015922991 2.277639370 -0.993177247 0.63834475
5996 -1.427476934 -0.915588035 -0.488613494 -1.793180314 -1.93872900
6003 0.850431505 -1.758120442 2.033010953 -3.021105222 0.29865646
6056 1.819492445 0.706555469 2.516051126 -0.582993152 1.02515838
6059 1.775043058 0.243942714 2.925288124 -1.464980535 0.09584433
6146 1.001385760 0.919774659 2.678496452 0.614407385 NA
6957 4.356845188 2.933668192 3.798730249 0.303177555 1.55883353
6958 NA NA NA NA NA
7013 1.269393348 0.231882711 1.845430766 -1.737664115 0.83937678
7014 -0.777065993 -0.173109207 1.106511930 -0.998522383 NA
7591 2.033212502 -1.062245157 3.591912559 -0.124300912 0.13395073
7592 0.041282899 0.769048622 NA NA NA
7593 1.992446225 0.518392355 2.456379783 -0.751141486 1.35644241
7594 1.582043136 0.809008992 1.918724842 -0.646937759 0.41290587
7595 2.191480477 0.602331676 2.880135864 -0.278893791 1.38060846
7596 0.031462460 0.520259656 1.169041371 -0.458712449 -0.36978907
7597 2.715823194 0.815873471 3.267650132 0.106207868 1.52925102
7598 2.162936865 1.071579930 2.600056189 -0.448678688 1.13792251
7655 2.415655144 0.754173812 2.924838637 -0.024829368 1.51653764
7880 1.911563338 0.524337295 2.399316173 -0.534362084 1.27817582
7997 1.049236982 -0.219877959 1.269302139 -0.920671007 -0.27314652
8180 3.176262340 1.970876797 4.536954777 0.796665238 2.06780812
8181 2.970585458 1.599822882 3.763376291 0.809400498 1.28231839
8182 2.011966076 1.135825156 2.893923520 -0.293938383 0.74017470
8392 1.748102948 0.731778356 2.597527405 -1.224045516 1.04772761
8490 0.670378936 -0.699439076 1.264241995 -2.186363359 -1.97609027
8491 -0.450103033 -0.513698351 1.646059660 0.103473366 NA
8492 1.687273065 0.430010372 2.416365462 -0.523637687 0.94254021
8754 -0.259928424 NA 0.703243118 -2.326262637 NA
8755 -0.241824272 -0.217728193 1.144485981 -2.403618724 NA
9075 2.297010424 0.461834972 2.967919200 -0.141100244 1.61036529
9076 NA NA NA NA NA
9655 1.793315876 -0.613961491 0.224727593 NA NA
9770 2.586217884 0.580914309 3.559823417 -0.823630915 1.38330182
9771 -0.169909187 -0.601308043 0.585472355 NA -0.89781180
10383 0.838550268 0.170043011 1.004541823 -0.284028238 1.02578256
10589 0.999026488 -1.093033295 1.319632335 -1.831449912 -1.31065420
10933 3.174346290 1.882396733 2.250025058 -0.263842402 2.15812666
11102 1.545560011 0.451882446 1.837889926 -0.629169855 0.75513543
11682 2.632362090 1.256918354 2.908020994 0.318463451 1.57914220
11683 2.433157017 -0.542301081 2.356882000 0.949649821 NA
11684 0.908083761 NA 2.876632423 NA NA
11685 0.762220873 -0.453293365 1.715411611 -0.137863617 0.76661506
11686 0.339994827 -0.745893991 2.015693279 -2.397438724 -0.70504633
12222 1.265467854 0.311304870 1.594556978 -0.799301008 0.89954349
12403 2.311844183 0.611316416 2.271764831 -0.738581393 NA
12550 0.887475557 0.133366664 1.375471034 -1.027713514 0.06958047
12551 2.697258019 0.672223992 2.931803098 -0.269406181 1.67049747
12986 0.186318537 0.568922845 NA NA NA
13408 0.333535954 NA 1.211106208 -1.158264756 -0.88208877
13409 1.041248790 -1.074450874 1.635314865 -1.318701912 -0.29207761
13410 2.590044489 0.793425413 3.628546912 -0.068679902 1.37849568
13477 NA NA NA NA NA
13639 1.485203587 -0.208488405 2.584498503 -0.941091103 0.64799723
14130 0.443588399 -0.681913945 1.442595883 -0.698551120 -0.26165629
14131 2.154726919 1.554897062 1.841821719 0.023916253 0.62031112
14134 1.702420885 NA 2.605030441 0.841921949 NA
tumorspinese AveExpr F P.Value adj.P.Val
104 -0.215095888 24.74296 NA NA NA
299 NA 22.59610 NA NA NA
300 -0.080107260 25.48954 2.6392489 2.492589e-02 4.801803e-02
509 2.646980532 27.79686 13.5820837 3.547638e-11 9.214690e-10
539 1.420373943 26.57984 5.1926623 4.861615e-04 1.639367e-03
540 NA 23.91343 NA NA NA
1587 NA 22.97096 NA NA NA
1693 NA 19.65769 NA NA NA
1694 -0.118011453 24.06733 NA NA NA
1900 2.025629110 25.30040 13.5891175 6.729588e-10 1.271426e-08
1956 1.261420235 25.52560 8.2487821 1.302837e-06 9.595520e-06
2170 NA 23.47002 NA NA NA
2171 1.717703609 26.56150 10.7081295 6.075523e-09 8.824052e-08
2212 0.555342616 25.90217 15.3834541 1.975708e-11 5.514833e-10
2213 NA 24.64078 NA NA NA
2245 -1.815142933 24.19952 NA NA NA
2618 -1.518008875 26.60715 6.6893501 8.547695e-06 4.952427e-05
2619 2.470017282 27.46826 20.1654360 3.737835e-15 2.682898e-13
2800 2.464570855 27.77018 31.2762301 4.430631e-20 1.017653e-17
2928 -0.383759898 24.18706 2.2360827 4.964559e-02 8.601313e-02
2939 2.471350389 26.40585 8.8630711 2.124100e-07 1.982082e-06
2984 2.681285197 28.37763 41.2506832 2.041548e-25 3.647111e-22
3007 NA 25.07856 NA NA NA
3020 2.840653143 27.49837 17.4205132 6.506551e-13 2.545312e-11
3565 NA 24.01018 NA NA NA
3583 1.313636944 24.71152 6.4260056 1.287498e-05 7.077055e-05
3678 2.388683706 27.72220 26.3621541 7.655047e-19 1.243211e-16
4547 2.888850679 26.14157 3.9060331 1.805586e-03 5.078764e-03
4548 0.507284323 25.11237 2.3620071 4.322549e-02 7.644698e-02
5532 1.347955817 27.35740 21.1460939 3.315683e-16 3.028952e-14
5566 2.180423840 28.16319 36.4329996 2.061702e-23 1.694347e-20
5573 -1.330770842 25.96103 NA NA NA
5575 1.883686991 27.98289 34.7851565 4.548291e-21 1.462548e-18
5576 0.570733579 25.11870 NA NA NA
5587 NA 20.72543 NA NA NA
5693 1.703467503 25.97999 NA NA NA
5694 1.590078009 27.75796 29.8239532 5.689803e-20 1.288460e-17
5695 NA 23.95533 NA NA NA
5769 2.692545507 26.87394 36.4183733 9.979503e-23 6.976107e-20
5770 1.793984321 26.06207 5.4645034 8.946055e-05 3.785123e-04
5793 NA 25.46879 NA NA NA
5883 1.036775758 24.36806 1.6098556 1.540712e-01 2.216496e-01
5891 0.250217035 20.63485 NA NA NA
5956 1.317689149 26.93209 36.9148496 4.017363e-23 3.075770e-20
5996 -1.351808230 22.44585 2.3384074 4.182511e-02 7.434650e-02
6003 1.208308749 28.05641 18.2938720 5.433737e-13 2.177299e-11
6056 2.248623590 28.08350 31.5269023 8.365676e-21 2.445515e-18
6059 2.135869710 27.33337 13.9166155 7.265259e-11 1.786098e-09
6146 1.619875181 24.31787 NA NA NA
6957 3.034749752 24.16154 4.5775991 4.380802e-04 1.494156e-03
6958 NA 21.99773 NA NA NA
7013 2.078949004 27.12965 16.3831265 1.180026e-11 3.526481e-10
7014 0.316997988 23.59250 NA NA NA
7591 -1.362501893 24.93905 12.3999297 3.838689e-09 5.950013e-08
7592 NA 24.60367 NA NA NA
7593 2.444774173 27.13801 25.9571976 1.652332e-18 2.459832e-16
7594 2.053041490 26.76752 7.0598121 9.127757e-06 5.256306e-05
7595 2.200588479 27.03271 21.6379066 5.115634e-16 4.544153e-14
7596 1.197207874 25.46546 1.6710400 1.466116e-01 2.129689e-01
7597 1.898139745 26.46628 2.5826729 2.709225e-02 5.145159e-02
7598 2.631640090 26.92089 23.5308111 1.932287e-16 1.806239e-14
7655 2.782397490 26.25220 34.3197653 2.385751e-22 1.237358e-19
7880 2.007026769 26.85540 29.3032932 2.830902e-20 6.793320e-18
7997 -0.175395833 25.26824 4.1656727 1.876368e-03 5.247563e-03
8180 2.737023197 25.97239 6.1513591 9.404861e-05 3.950140e-04
8181 3.098592272 24.57187 9.8699224 3.833294e-07 3.315315e-06
8182 2.433031501 26.92438 4.9152549 3.753229e-04 1.307855e-03
8392 1.623009038 26.20737 6.1984920 2.604153e-05 1.310882e-04
8490 1.000586113 26.76932 11.2460833 9.784444e-09 1.352659e-07
8491 NA 24.24754 NA NA NA
8492 2.076909654 26.54061 20.8273352 4.488117e-15 3.164910e-13
8754 NA 25.12870 NA NA NA
8755 NA 24.49555 NA NA NA
9075 2.399742570 26.07972 8.9794803 1.522708e-07 1.479281e-06
9076 NA 22.37858 NA NA NA
9655 NA 22.04215 NA NA NA
9770 2.405216934 26.80442 17.2571146 2.721536e-13 1.192896e-11
9771 0.198896864 23.90441 NA NA NA
10383 1.496398319 25.35084 2.1157733 6.721944e-02 1.109604e-01
10589 0.245917900 25.57837 14.2363653 2.367751e-10 5.114280e-09
10933 1.779042372 25.92375 4.3781436 6.992998e-04 2.241942e-03
11102 1.127230490 25.77383 3.7452816 3.111996e-03 8.131753e-03
11682 3.068976762 25.72869 6.1072857 4.463015e-05 2.085334e-04
11683 2.699749504 24.69878 NA NA NA
11684 NA 24.09139 NA NA NA
11685 0.558393781 25.40977 4.2036155 8.688784e-04 2.700005e-03
11686 NA 25.15923 NA NA NA
12222 1.686509924 26.29754 24.3815955 2.509760e-17 2.823081e-15
12403 1.316355957 26.39076 NA NA NA
12550 0.431283875 25.82923 8.4668660 2.267989e-06 1.558988e-05
12551 2.577694733 27.08897 31.9386618 3.082903e-21 1.101487e-18
12986 NA 23.69576 NA NA NA
13408 NA 24.96392 NA NA NA
13409 0.113973397 25.15299 3.5161031 4.367378e-03 1.087759e-02
13410 2.701054010 26.30801 32.0757307 7.919440e-20 1.697717e-17
13477 NA 22.77744 NA NA NA
13639 1.763785992 25.85677 5.5673312 1.424127e-04 5.660599e-04
14130 -1.010947705 24.62907 1.1557283 3.453820e-01 4.306360e-01
14131 1.265053848 24.14763 7.6309883 5.708969e-06 3.496716e-05
14134 NA 24.05487 NA NA NA
[ reached getOption("max.print") -- omitted 403 rows ]pept_id <- unlist( as.integer( rownames_to_column(topTable(eBayes(fit), coef = 3:8, number = Inf)[1:2000,])[,1] ) )
pept_id_q <- unlist( as.integer( rownames_to_column(topTable(eBayes(fit_quant), coef = 3:8, number = Inf)[1:2000,])[,1] ) )
peptides_fit <- peptides_83_narm$`Leading razor protein`[pept_id]Pectin
pectin <- peptides_83_narm[ peptides_83_narm$`Leading razor protein` == "Q15149",1]
pectin <- as.integer( unlist( pectin ) )
grep( "Q15149", peptides_fit ) [1] 178 179 369 390 399 505 678 739 974 1223 1813ggplot( cbind( mf_83, expr=pept83_vsn[rownames(pept83_vsn)==pectin[10],] ) )+geom_beeswarm( aes( x=tumor, y=expr, col=tumor, shape=processing ) )
pheatmap(
pept83_vsn[ rownames( pept83_vsn )%in%pectin, order( mf_83$tumor ) ], colorRampPalette(brewer.pal( 11,"PuOr" ) )(100), cluster_rows = FALSE, cluster_cols = FALSE, na_col = "grey" , annotation_col = tumor )
Beta-1,3-glucosyltransferase
ggplot( cbind( mf_83, expr=pept83_vsn[rownames(pept83_vsn)==54363,] ) )+geom_beeswarm( aes( x=tumor, y=expr, col=tumor, shape=processing ) )
peptides_83$`Leading razor protein`[54363][1] "Q6Y288"gtransferase <- peptides_83_narm[ peptides_83_narm$`Leading razor protein` == "Q6Y288",1]
gtransferase <- as.integer( unlist(gtransferase ) )
pheatmap(
pept83_vsn[ rownames( pept83_vsn )%in%gtransferase, order( mf_83$tumor ) ], colorRampPalette(brewer.pal( 11,"PuOr" ) )(100), cluster_rows = FALSE, cluster_cols = FALSE, na_col = "grey", annotation_col = tumor )
Protein S100-B
peptides_83$`Leading razor protein`[5035][1] "P04271"ggplot( cbind( mf_83, expr=pept83_vsn[rownames(pept83_vsn)==5035,] ) )+geom_beeswarm( aes( x=tumor, y=expr, col=tumor, shape=processing ) )
gtransferase <- peptides_83_narm[ peptides_83_narm$`Leading razor protein` == "P04271",1]
gtransferase <- as.integer( unlist(gtransferase ) )
pheatmap(
pept83_vsn[ rownames( pept83_vsn )%in%gtransferase, order( mf_83$tumor ) ], colorRampPalette(brewer.pal( 11,"PuOr" ) )(100), cluster_rows = FALSE, cluster_cols = FALSE, na_col = "grey", annotation_col = tumor )
Gamma-interferon-inducible lysosomal thiol reductase
peptides_83$`Leading razor protein`[25517][1] "P13284"ggplot( cbind( mf_83, expr=pept83_vsn[rownames(pept83_vsn)==25517,] ) ) + geom_beeswarm( aes( x=tumor, y=expr, col=tumor, shape=processing ) )
treductase <- peptides_83_narm[ peptides_83_narm$`Leading razor protein` == "P13284",1]
treductase <- as.integer( unlist(treductase ) )
pheatmap(
pept83_vsn[ rownames( pept83_vsn )%in%treductase, order( mf_83$tumor )], colorRampPalette(brewer.pal( 11,"PuOr" ) )(100), cluster_rows = FALSE, cluster_cols = FALSE, na_col = "grey", annotation_col = tumor )
Galectin-1
peptides_83$`Leading razor protein`[61029][1] "P09382"ggplot( cbind( mf_83, expr=pept83_vsn[rownames(pept83_vsn)==61029,] ) ) + geom_beeswarm( aes( x=tumor, y=expr, col=tumor, shape=processing ) )
galectin <- peptides_83_narm[ peptides_83_narm$`Leading razor protein` == "P09382",1]
galectin <- as.integer( unlist( galectin ) )
pheatmap(
pept83_vsn[ rownames( pept83_vsn )%in%galectin, order( mf_83$tumor ) ], colorRampPalette(brewer.pal( 11,"PuOr" ) )(100), cluster_rows = FALSE, cluster_cols = FALSE, na_col = "grey", annotation_col = tumor )
Fructosamine-3-kinase
peptides_83$`Leading razor protein`[42567][1] "Q9H479"ggplot( cbind( mf_83, expr=pept83_vsn[rownames(pept83_vsn)==42567,] ) ) + geom_beeswarm( aes( x=tumor, y=expr, col=tumor, shape=processing ) )
fkinase <- peptides_83_narm[ peptides_83_narm$`Leading razor protein` == "Q9H479",1]
fkinase <- as.integer( unlist( fkinase ) )
pheatmap(
pept83_vsn[ rownames( pept83_vsn )%in%fkinase, order(mf_83$tumor) ], colorRampPalette(brewer.pal( 11,"PuOr" ) )(100), cluster_rows = FALSE, cluster_cols = FALSE, na_col = "grey", annotation_col = tumor )
Selenium-binding protein 1
peptides_83$`Leading razor protein`[47619][1] "Q13228"ggplot( cbind( mf_83, expr=pept83_vsn[rownames(pept83_vsn)==47619,] ) ) + geom_beeswarm( aes( x=tumor, y=expr, col=tumor, shape=processing ) )
seleniumprot <- peptides_83_narm[ peptides_83_narm$`Leading razor protein` == "Q13228",1]
seleniumprot <- as.integer( unlist( seleniumprot ) )
pheatmap(
pept83_vsn[ rownames( pept83_vsn )%in%seleniumprot, order(mf_83$tumor) ], colorRampPalette(brewer.pal( 11,"PuOr" ) )(100), cluster_rows = FALSE, cluster_cols = FALSE, na_col = "grey", annotation_col = tumor )
Calcyphosin
peptides_83$`Leading razor protein`[13763][1] "Q13938"ggplot( cbind( mf_83, expr=pept83_vsn[rownames(pept83_vsn)==13763,] ) ) + geom_beeswarm( aes( x=tumor, y=expr, col=tumor, shape=processing ) )
calcyphosin <- peptides_83_narm[ peptides_83_narm$`Leading razor protein` == "Q13228",1]
calcyphosin <- as.integer( unlist( calcyphosin ) )
pheatmap(
pept83_vsn[ rownames( pept83_vsn )%in%calcyphosin, order(mf_83$tumor) ], colorRampPalette(brewer.pal( 11,"PuOr" ) )(100), cluster_rows = FALSE, cluster_cols = FALSE, na_col = "grey", annotation_col = tumor )
Thymosin beta-4;Hematopoietic system regulatory peptide
peptides_83$`Leading razor protein`[60164][1] "P62328"ggplot( cbind( mf_83, expr=pept83_vsn[rownames(pept83_vsn)==60164,] ) ) + geom_beeswarm( aes( x=tumor, y=expr, col=tumor, shape=processing ) )
thymosinbeta <- peptides_83_narm[ peptides_83_narm$`Leading razor protein` == "P62328",1]
thymosinbeta <- as.integer( unlist( thymosinbeta ) )
pheatmap(
pept83_vsn[ rownames( pept83_vsn )%in%thymosinbeta, order(mf_83$tumor) ], colorRampPalette(brewer.pal( 11,"PuOr" ) )(100), cluster_rows = FALSE, cluster_cols = FALSE, na_col = "grey", annotation_col = tumor )
Phosphatidylethanolamine-binding protein 1;Hippocampal cholinergic neurostimulating peptide
peptides_83$`Leading razor protein`[74265][1] "P30086"ggplot( cbind( mf_83, expr=pept83_vsn[rownames(pept83_vsn)==74265,] ) ) + geom_beeswarm( aes( x=tumor, y=expr, col=tumor, shape=processing ) )
phosphatprot <- peptides_83_narm[ peptides_83_narm$`Leading razor protein` == "P30086",1]
phosphatprot <- as.integer( unlist( phosphatprot ) )
pheatmap(
pept83_vsn[ rownames( pept83_vsn )%in%phosphatprot, order(mf_83$tumor) ], colorRampPalette(brewer.pal( 11,"PuOr" ) )(100), cluster_rows = FALSE, cluster_cols = FALSE, na_col = "grey", annotation_col = tumor )
pheatmap(
pept83_vsn[ rownames( pept83_vsn )%in%pept_id[1:1000], order( mf_83$tumor ) ], colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = FALSE, cluster_cols = FALSE, na_col = "grey" , annotation_col = tumor)
pheatmap(
pept83_vsn[ rownames( pept83_vsn )%in%pept_id[1:1000], order( mf_83$tumor ) ], colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = FALSE, cluster_cols = TRUE, na_col = "grey" , annotation_col = tumor)
compute distance matrix and run tsne
dist83 <- dist( t( pept83_vsn ) )
dist83_man <- dist( t( pept83_vsn ), method = "manhattan" )
tsne_83 <- tsne( dist83, perplexity = 30 )sigma summary: Min. : 3.27522826755687 |1st Qu. : 5.42710321565532 |Median : 5.81757147096079 |Mean : 5.88958645911205 |3rd Qu. : 6.34346507708088 |Max. : 8.42691327818654 |
Epoch: Iteration #100 error is: 13.1418994953574
Epoch: Iteration #200 error is: 0.278537013176371
Epoch: Iteration #300 error is: 0.24380179008483
Epoch: Iteration #400 error is: 0.241884335088339
Epoch: Iteration #500 error is: 0.240904259625756
Epoch: Iteration #600 error is: 0.240712141476871
Epoch: Iteration #700 error is: 0.240706834306753
Epoch: Iteration #800 error is: 0.240706832997349
Epoch: Iteration #900 error is: 0.24070683299678
Epoch: Iteration #1000 error is: 0.24070683299678tsne_83 <- as.tibble( tsne_83 ) %>%
mutate( tag = mf_83$tag ) %>%
left_join( mf_83 )Joining, by = "tag"tsne_83_p40 <- tsne( dist83, perplexity = 40 )sigma summary: Min. : 3.59988080425995 |1st Qu. : 5.88735723905951 |Median : 6.36356353884777 |Mean : 6.39175395062142 |3rd Qu. : 6.85321747485905 |Max. : 8.96479709973485 |
Epoch: Iteration #100 error is: 12.7741445595454
Epoch: Iteration #200 error is: 0.267633319856596
Epoch: Iteration #300 error is: 0.200333444272339
Epoch: Iteration #400 error is: 0.198155552025055
Epoch: Iteration #500 error is: 0.197907856922041
Epoch: Iteration #600 error is: 0.198116615566683
Epoch: Iteration #700 error is: 0.198669486171787
Epoch: Iteration #800 error is: 0.197826557418638
Epoch: Iteration #900 error is: 0.198077936210623
Epoch: Iteration #1000 error is: 0.198123816192984tsne_83_p40 <- as.tibble( tsne_83_p40 ) %>%
mutate( tag = mf_83$tag ) %>%
left_join( mf_83 )Joining, by = "tag"tsne_83_p50 <- tsne( dist83, perplexity = 50 )sigma summary: Min. : 3.93209738892534 |1st Qu. : 6.36296166987278 |Median : 6.86383197809738 |Mean : 6.89628555781445 |3rd Qu. : 7.37565924102249 |Max. : 9.48682054042479 |
Epoch: Iteration #100 error is: 11.8665211616943
Epoch: Iteration #200 error is: 0.221588009161539
Epoch: Iteration #300 error is: 0.197441300801638
Epoch: Iteration #400 error is: 0.197105658685948
Epoch: Iteration #500 error is: 0.197807396959868
Epoch: Iteration #600 error is: 0.197047645028721
Epoch: Iteration #700 error is: 0.197052568368569
Epoch: Iteration #800 error is: 0.197097812170133
Epoch: Iteration #900 error is: 0.197051246943868
Epoch: Iteration #1000 error is: 0.197986514328785tsne_83_p50 <- as.tibble( tsne_83_p50 ) %>%
mutate( tag = mf_83$tag ) %>%
left_join( mf_83 )Joining, by = "tag"tsne_83_p20 <- tsne( dist83, perplexity = 20 )sigma summary: Min. : 2.92232873106458 |1st Qu. : 4.8249063318583 |Median : 5.29944326162149 |Mean : 5.33020784726405 |3rd Qu. : 5.78127727533432 |Max. : 7.79776120242705 |
Epoch: Iteration #100 error is: 14.1244797912169
Epoch: Iteration #200 error is: 1.05227216100461
Epoch: Iteration #300 error is: 1.39185572111483
Epoch: Iteration #400 error is: 0.865561671436752
Epoch: Iteration #500 error is: 0.928210799428743
Epoch: Iteration #600 error is: 0.78920908082322
Epoch: Iteration #700 error is: 0.707223672264703
Epoch: Iteration #800 error is: 0.626912950999438
Epoch: Iteration #900 error is: 0.521177964889802
Epoch: Iteration #1000 error is: 0.380256757990375tsne_83_p20 <- as.tibble( tsne_83_p20 ) %>%
mutate( tag = mf_83$tag ) %>%
left_join( mf_83 )Joining, by = "tag"tsne_83_p15 <- tsne( dist83, perplexity = 15 )sigma summary: Min. : 2.71366127177117 |1st Qu. : 4.47116300241942 |Median : 4.96813794283053 |Mean : 4.98742524748465 |3rd Qu. : 5.43966277329089 |Max. : 7.39135875751793 |
Epoch: Iteration #100 error is: 13.533146332584
Epoch: Iteration #200 error is: 1.82119862518867
Epoch: Iteration #300 error is: 1.59331784588475
Epoch: Iteration #400 error is: 1.26007517471072
Epoch: Iteration #500 error is: 0.918154259678304
Epoch: Iteration #600 error is: 0.628372838196198
Epoch: Iteration #700 error is: 0.397149600652831
Epoch: Iteration #800 error is: 0.350050822167342
Epoch: Iteration #900 error is: 0.341935100434109
Epoch: Iteration #1000 error is: 0.339556079510723tsne_83_p15 <- as.tibble( tsne_83_p15 ) %>%
mutate( tag = mf_83$tag ) %>%
left_join( mf_83 )Joining, by = "tag"tsne_83m <- tsne( dist83_man )sigma summary: Min. : 50.2874877800196 |1st Qu. : 79.4463708983878 |Median : 85.9061800835288 |Mean : 86.4945301580699 |3rd Qu. : 93.8433836914378 |Max. : 126.017720734057 |
Epoch: Iteration #100 error is: 13.4121480825776
Epoch: Iteration #200 error is: 0.333850534518615
Epoch: Iteration #300 error is: 0.324774950145486
Epoch: Iteration #400 error is: 0.321051728113649
Epoch: Iteration #500 error is: 0.320834775869847
Epoch: Iteration #600 error is: 0.317721171175521
Epoch: Iteration #700 error is: 0.317493804719201
Epoch: Iteration #800 error is: 0.318487007308581
Epoch: Iteration #900 error is: 0.31856023141532
Epoch: Iteration #1000 error is: 0.324267161135035tsne_83m <- as.tibble( tsne_83m ) %>%
mutate( tag = mf_83$tag ) %>%
left_join( mf_83 )Joining, by = "tag"ggplot( tsne_83 ) +
geom_point( aes( x=V1, y=V2, col=tumor ), size=3 ) +
scale_colour_manual( name="", values = colorRampPalette(brewer.pal( 8,"Accent" ))(8) )
ggplot( tsne_83m ) +
geom_point( aes( x=V1, y=V2, col=tumor ), size=3 ) +
scale_colour_manual( name="", values = colorRampPalette(brewer.pal( 8,"Accent" ))(8) )
remove pfb samples and run dist, tsne and heatmap clustering again
pept83_vsn_pfb <- pept83_vsn[,mf_83$tumor!="pfb"]
dist83_pfb <- dist( t( pept83_vsn_pfb ) )
tsne_83_pfb <- tsne( dist83_pfb, perplexity = 25 )sigma summary: Min. : 4.19661557974886 |1st Qu. : 5.37186304994619 |Median : 5.78873923085712 |Mean : 5.85809526495935 |3rd Qu. : 6.25240865044914 |Max. : 8.35981042160989 |
Epoch: Iteration #100 error is: 18.2383665292166
Epoch: Iteration #200 error is: 1.73227702079011
Epoch: Iteration #300 error is: 1.27509854118552
Epoch: Iteration #400 error is: 1.01807868893286
Epoch: Iteration #500 error is: 0.8407368975744
Epoch: Iteration #600 error is: 0.785139679760407
Epoch: Iteration #700 error is: 0.711252673063384
Epoch: Iteration #800 error is: 0.640472461678369
Epoch: Iteration #900 error is: 0.55279920113952
Epoch: Iteration #1000 error is: 0.451214001987613tsne_83_pfb <- as.tibble( tsne_83_pfb ) %>%
mutate( tag = mf_83$tag[mf_83$tumor!="pfb"] ) %>%
left_join( mf_83 )Joining, by = "tag"ggplot( tsne_83_pfb ) +
geom_point( aes( x=V1, y=V2, col=tumor ), size=3 ) +
scale_colour_manual( name="", values = colorRampPalette(brewer.pal( 8,"Accent" ))(8) )
redo the fit on the pfb removed vsn data
mm_pfb <- model.matrix( ~ processing + tumor , mf_83[mf_83$tumor!="pfb",] )
fit_pfb <- lmFit( pept83_vsn_pfb, mm_pfb )Partial NA coefficients for 53388 probe(s)pept_id_pfb <- unlist( as.integer( rownames_to_column(topTable(eBayes(fit_pfb), coef = 3:7, number = Inf)[1:2000,])[,1] ) )
pheatmap(
pept83_vsn_pfb[ rownames( pept83_vsn_pfb )%in%pept_id_pfb[1:1000], order( mf_83$tumor[mf_83$tumor!="pfb"] ) ], colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = FALSE, cluster_cols = TRUE, na_col = "grey" , annotation_col = tumor)
redo the fit on the pfb removed quantile normalized data
pept83_quant_pfb <- pept83_quant_log[ , mf_83$tumor!="pfb" ]
mm_pfb <- model.matrix( ~ processing + tumor , mf_83[mf_83$tumor!="pfb",] )
fit_quant_pfb <- lmFit( pept83_quant_pfb, mm_pfb )Partial NA coefficients for 53388 probe(s)pept_id_quant_pfb <- unlist( as.integer( rownames_to_column(topTable(eBayes(fit_quant_pfb), coef = 3:7, number = Inf)[1:2000,])[,1] ) )
pheatmap(
pept83_quant_pfb[ rownames( pept83_quant_pfb )%in%pept_id_quant_pfb[1:1000], order( mf_83$tumor[mf_83$tumor!="pfb"] ) ], colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = FALSE, cluster_cols = TRUE, na_col = "grey" , annotation_col = tumor)
remove pfb samples and run dist, tsne and heatmap clustering again (for quantile normalized data)
dist83_quant_pfb <- dist( t( pept83_quant_pfb ) )
tsne83_quant_pfb <- tsne( dist83_quant_pfb, perplexity = 25 )sigma summary: Min. : 4.26016304848391 |1st Qu. : 5.325280763332 |Median : 5.80174412845819 |Mean : 5.89245061142049 |3rd Qu. : 6.24766670423193 |Max. : 8.83600365984668 |
Epoch: Iteration #100 error is: 16.5346263195079
Epoch: Iteration #200 error is: 1.75700464374134
Epoch: Iteration #300 error is: 1.31872581984812
Epoch: Iteration #400 error is: 0.946609956219227
Epoch: Iteration #500 error is: 0.713581433166488
Epoch: Iteration #600 error is: 0.540232255969527
Epoch: Iteration #700 error is: 0.430407810740797
Epoch: Iteration #800 error is: 0.368857161443051
Epoch: Iteration #900 error is: 0.349011938782488
Epoch: Iteration #1000 error is: 0.344531567271845tsne83_quant_pfb <- as.tibble( tsne83_quant_pfb ) %>%
mutate( tag = mf_83$tag[mf_83$tumor!="pfb"] ) %>%
left_join( mf_83 )Joining, by = "tag"ggplot( tsne83_quant_pfb ) +
geom_point( aes( x=V1, y=V2, col=tumor ), size=3 ) +
scale_colour_manual( name="", values = colorRampPalette(brewer.pal( 8,"Accent" ))(8) )
find peptides that differ most from one tumor group (with respect to the other groups)
mean_relpos <- rowMeans( pept83_vsn[, mf_83$tumor=="relpos"], na.rm = TRUE )
mean_pept_relpos <- rowMeans( pept83_vsn[, mf_83$tumor!="relpos"], na.rm =TRUE )
dmean_relpos <- mean_pept_relpos - mean_relpos %>%
as.data.frame()
rownames( dmean_relpos ) = rownames( pept83_vsn )
dmean_relpos <- rownames_to_column( dmean_relpos )
dmean_relpos <- rename( dmean_relpos, mean_diff = . )
pept83_relpos_id <- order( desc( abs( dmean_relpos$mean_diff ) ) )[1:5000]
pept83_relpos <- peptides_83[dmean_relpos$rowname[pept83_relpos_id],]
write.table( unique( pept83_relpos$`Leading razor protein` ), "peptides_relpos", row.names=FALSE, quote=FALSE )
write.table( unique( peptides_83$`Leading razor protein` ), "peptides83", row.names=FALSE, quote=FALSE )heatmap of the distance matrix
pheatmap( dist83_pfb, colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = TRUE, cluster_cols = TRUE, na_col = "grey" , annotation_col = tumor)
---
title: "R Notebook"
output: html_notebook
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*. 

```{r}
library(readr)
library(readxl)
library(pheatmap)
library(RColorBrewer)
library(limma)
library(XML)
library(genefilter)
library(cowplot)
library(vsn)
library(preprocessCore)
library(Rcpp)
library(rje)
library(xlsx)
library(geneplotter)
library(tidyverse)
library(ggbeeswarm)
library(tsne)

if (!require("devtools")) {
  install.packages("devtools", dependencies = TRUE)
  library(devtools)
}
install_github("raivokolde/pheatmap")
```

First step: load the data (Peptides table)
```{r}

peptides_83 <- read_delim("/mnt/Data/Anders_group/u/annika/83samples/20180611_EPNfull_Annika_peptides.txt", "\t", escape_double = FALSE, trim_ws = TRUE)
peptides_83 <- rownames_to_column( peptides_83 )
peptides_83[1:10,1:20]

```

Prepare the data for working with it (83 samples)
```{r}

pept83_corr_backup <- peptides_83  %>%
  dplyr::select( starts_with("Intensity") ) %>%
  dplyr::select( -contains("Library") ) %>%
  rename_all( str_replace, "Intensity ", "" )  %>%
  rename_all( str_replace, "_QE1_TM_fullEPN", "" )  %>%
  rename_all( str_replace, "70915_QE1_TM_EPN_", "" )  %>%
  rename_all( str_replace, "_1_", "_" ) %>%
  rename_all( str_replace, "80416_", "" )  %>%  
  rename_all( str_replace, "80413_", "" ) %>%
  dplyr::select( -contains("Intensity") ) 
pept83_corr_backup[pept83_corr_backup==0]=NA
pept83_corr_backup=as.matrix( pept83_corr_backup )

pept83_corr <- log2( pept83_corr_backup[ rowSums( is.na( pept83_corr_backup ) )!=ncol( pept83_corr_backup ), ] )

pept83_corr2 <- pept83_corr_backup[ rowSums( is.na( pept83_corr_backup ) )!=ncol( pept83_corr_backup ), ] 
```

tumor groups and associated samples
```{r}
pfa = c( "7EP34", "4EP29", "9EP29", "4EP5", "7EP18", "3EP15", "3EP18", "3EP22", "3EP9", "3EP26", "3EP55", "3EP73", "3EP74", "4EP24", "9EP9", "9EP1", "9EP33", "7EP3", "7EP4", "7EP11", "11EP32", "7EP32", "7EP48", "7EP26" )
relpos = c( "4EP49", "3EP19", "4EP48", "3EP30", "4EP54", "7EP35", "4EP51", "3EP50", "3EP28", "3EP8", "9EP13", "4EP47", "3EP11", "3EP54", "3EP67", "3EP72", "4EP53", "4EP44", "7EP1", "7EP9" )
pfb = c( "3EP2", "3EP7", "3EP49", "4EP3", "4EP26", "4EP32", "4EP35", "9EP30", "7EP5", "7EP49", "7EP15", "7EP22")
mpe = c( "13EP1", "13EP2", "13EP3", "5EP21", "13EP4", "13EP5", "13EP6" )
pfse = c( "7EP19", "11EP7", "13EP8", "13EP9", "13EP12", "13EP14", "13EP18" )
spine = c( "NCH439A", "NCH678N", "NCH752I", "NCH1594", "NCH4384" )
spinese = c( "13EP13", "NCH2838", "NCH4121" )
stse = c( "3EP3", "3EP46", "3EP75", "13EP10", "13EP11" )
```


Create model frame (17 samples)
```{r}
mf_83 <- as.tibble( colnames( pept83_corr_backup ) ) %>%
  rename( tag = value ) %>%
  mutate( replicate = ifelse( str_detect( tag, "R2"), "2", "1" ) ) %>%
  mutate( processing = ifelse( str_detect( tag, "T2"), "old", "new" ) ) %>%
  mutate( techn_repl = ifelse( str_detect( tag, "R1-2"), "yes", "no" ) ) %>%
  mutate( sample = str_remove( tag, "R2_" ) ) %>%
  mutate( sample = str_remove( sample, "_T2" ) ) %>%
  mutate( sample = str_remove( sample, "old_" ) ) %>%
  mutate( sample = str_remove( sample, "R1-2_" ) ) %>%
  mutate( tumor = ifelse( sample%in%relpos, "relpos", "0" ) ) %>%
  mutate( tumor = ifelse( sample%in%pfa, "pfa", tumor ) ) %>%
  mutate( tumor = ifelse( sample%in%pfb, "pfb", tumor ) ) %>%
  mutate( tumor = ifelse( sample%in%mpe, "mpe", tumor ) ) %>%
  mutate( tumor = ifelse( sample%in%pfse, "pfse", tumor ) ) %>%
  mutate( tumor = ifelse( sample%in%spine, "spine", tumor ) ) %>%
  mutate( tumor = ifelse( sample%in%spinese, "spinese", tumor ) ) %>%
  mutate( tumor = ifelse( sample%in%stse, "stse", tumor ) )

```


have a first look at the clustering of the new measured samples
```{r}
brks <- ( seq(0, 1, length.out=50)[-1] )^0.7
n = mf_83$processing=="new" & mf_83$replicate=="1"

tumor <- as.matrix( mf_83[,6] )
rownames( tumor ) <- mf_83$tag 
tumor <- as.data.frame( tumor )

pheatmap( 
   cor(pept83_corr[ n,n ], use = "p"), colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = TRUE, cluster_cols = TRUE, breaks=c(-rev(brks),0,brks), na_col = "grey" , annotation_col = tumor )
```


compare old and new measured 17 samples
```{r}
samples17 = mf_83$sample[mf_83$processing=="old"]

processing <- as.matrix( mf_83[,3] )
rownames( processing ) <- mf_83$tag 
processing <- as.data.frame( processing )

pheatmap( 
   cor(pept83_corr[ , mf_83$sample%in%samples17 ], use = "p"), colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = TRUE, cluster_cols = TRUE, breaks=c(-rev(brks),0,brks), na_col = "grey" , annotation_col = tumor, annotation_row =  processing)

```


multidensity plots of 17 samples
```{r}
multidensity( pept83_corr[,mf_83$sample%in%samples17], legend=FALSE,  col=c("black", "blue")[ as.integer( factor( mf_83$processing[mf_83$sample%in%samples17] ) ) ] )

multidensity( pept83_corr[,mf_83$sample%in%samples17], legend=FALSE,  col=c("black", "blue")[ as.integer( factor( mf_83$tumor[mf_83$sample%in%samples17] ) ) ] )
```

Boxplot of 17 samples
```{r}
boxplot(pept83_corr[,mf_83$sample%in%samples17], legend=FALSE, col=c("black", "blue")[ as.integer( factor( mf_83$processing ) ) ] )
```

vsn normalization of 17 samples
```{r}
peptides_83_narm <- peptides_83[rowSums( is.na( pept83_corr_backup ) )!=ncol( pept83_corr_backup ),]
pept83_vsn <- normalizeVSN( pept83_corr2 ) 
rownames( pept83_vsn ) <- unlist( peptides_83_narm[,1] )

multidensity( pept83_vsn[,mf_83$sample%in%samples17], legend=FALSE,  col=c("black", "blue")[ as.integer( factor( mf_83$processing ) ) ] )

pheatmap( 
   cor( pept83_vsn[ , mf_83$sample%in%samples17 ], use = "p" ), colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = TRUE, cluster_cols = TRUE, breaks=c(-rev(brks),0,brks), na_col = "grey" , annotation_col = tumor, annotation_row = processing )

```

quantile normalization of 17 samples (on log2 scale and original data)
 -> log2 scale -> enhances differences between old and new processed samples
```{r}
pept83_quant <- normalize.quantiles( pept83_corr2 )
colnames( pept83_quant ) <- mf_83$tag

pept83_quant_log <- log2( normalize.quantiles( pept83_corr2 ) )
colnames( pept83_quant_log ) <- mf_83$tag
rownames( pept83_quant_log ) <- unlist( peptides_83_narm[,1] )

multidensity( pept83_quant[,mf_83$sample%in%samples17], legend=FALSE,  col=c("black", "blue")[ as.integer( factor( mf_83$processing ) ) ] )

pheatmap( 
   cor( pept83_quant[ , mf_83$sample%in%samples17 ], use = "p" ), colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = TRUE, cluster_cols = TRUE, breaks=c(-rev(brks),0,brks), na_col = "grey" , annotation_col = tumor, annotation_row = processing )

pheatmap( 
   cor( pept83_quant_log[ , mf_83$sample%in%samples17 ], use = "p" ), colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = TRUE, cluster_cols = TRUE, breaks=c(-rev(brks),0,brks), na_col = "grey" , annotation_col = tumor, annotation_row = processing )
```

Compare replicates and original samples
```{r}
replicates <- mf_83$sample[mf_83$replicate=="2"]

multidensity( pept83_corr[,mf_83$sample%in%replicates], legend=FALSE,  col=c("black", "blue")[ as.integer( factor( mf_83$replicate[mf_83$sample%in%replicates] ) ) ] )

repl <- as.matrix( mf_83[,2] )
rownames( repl ) <- mf_83$tag 
repl <- as.data.frame( repl )

pheatmap( 
   cor( pept83_corr[ , mf_83$sample%in%replicates ], use = "p" ), colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = TRUE, cluster_cols = TRUE, breaks=c(-rev(brks),0,brks), na_col = "grey" , annotation_col = tumor, annotation_row = repl )

pheatmap( 
   cor( pept83_quant_log[ , mf_83$sample%in%replicates ], use = "p" ), colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = TRUE, cluster_cols = TRUE, breaks=c(-rev(brks),0,brks), na_col = "grey" , annotation_col = tumor, annotation_row = repl )
```

Missing values
```{r}

r1 <- mf_83$replicate=="2"
r2 <- mf_83$replicate=="1"&mf_83$sample%in%replicates

misses <- as.data.frame( 
  c(  pept83_corr[rowSums(is.na(pept83_corr[,r1]))!=ncol(pept83_corr[,r1]),r1] %>%
    apply( 2, function(x) sum(is.na(x))),
  pept83_corr[rowSums(is.na(pept83_corr[,r2]))!=ncol(pept83_corr[,r2]),r2] %>%
    apply( 2, function(x) sum(is.na(x))) ) )
misses <- rownames_to_column(misses) 
colnames(misses) <- c("tag","missing_values") 
misses <- left_join(misses, mf_83[mf_83$sample%in%replicates,])

ord83 <- order( misses$missing_values[misses$replicate=="2"] )

ggplot( misses, aes( x=sample, y=missing_values, col=replicate, group=replicate ) ) +
  geom_point() +
  geom_smooth( method="lm", se=FALSE ) +
  scale_x_discrete( limits=misses$sample[misses$replicate=="2"][ord83] ) 
```

calculate number of rows containing only NA 
```{r}
sum(rowSums(is.na(pept83_corr_backup))==ncol(pept83_corr_backup))

```


correlation heatmap of quantile normalized and raw data
```{r}
pheatmap( 
   cor(pept83_quant_log[ n,n ], use = "p"), colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = TRUE, cluster_cols = TRUE, breaks=c(-rev(brks),0,brks), na_col = "grey" , annotation_col = tumor )

pheatmap( 
   cor(pept83_corr_backup[ n,n ], use = "p"), colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = TRUE, cluster_cols = TRUE, breaks=c(-rev(brks),0,brks), na_col = "grey" , annotation_col = tumor )
```

Create linear model 
```{r}
mm <- model.matrix( ~ processing + tumor , mf_83 )
fit <- lmFit( pept83_vsn, mm )
fit_quant <- lmFit( pept83_quant_log, mm )
coeff83 <- fit$coefficients
```


plot correlation and peptides that can be found in all samples (coefficients fit)
```{r}
pheatmap( 
   cor( coeff83[ complete.cases(coeff83), 2:8], use = "p" ), colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = TRUE, cluster_cols = TRUE, breaks=c(-rev(brks),0,brks), na_col = "grey" )

pheatmap( 
  coeff83[ complete.cases(coeff83), 2:8], colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = TRUE, cluster_cols = TRUE, breaks=c(-rev(brks),0,brks), na_col = "grey" )
```

plot peptides with highest SD (coefficients fit)
```{r}
sd=apply( coeff83[complete.cases(coeff83),2:8], 1, sd, na.rm = TRUE )
ord_sd = order(sd, decreasing = TRUE)

pheatmap( 
  coeff83[complete.cases(coeff83),][ord_sd[1:100], 2:8], colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = FALSE, cluster_cols = TRUE, na_col = "grey" )
```


```{r}
a <- peptides_83_narm[ peptides_83_narm$`Leading razor protein` == "Q15149",1]
a <- as.integer( unlist( a ) )

pept_id <- unlist( as.integer( rownames_to_column(topTable(eBayes(fit), coef = 3:8, number = Inf)[1:2000,])[,1] ) )
pept_id_q <- unlist( as.integer( rownames_to_column(topTable(eBayes(fit_quant), coef = 3:8, number = Inf)[1:2000,])[,1] ) )

peptides_fit <- peptides_83_narm$`Leading razor protein`[pept_id]
```

Pectin
```{r}
pectin <- peptides_83_narm[ peptides_83_narm$`Leading razor protein` == "Q15149",1]
pectin <- as.integer( unlist( pectin ) )
grep( "Q15149", peptides_fit )

ggplot( cbind( mf_83, expr=pept83_vsn[rownames(pept83_vsn)==pectin[10],] ) )+geom_beeswarm( aes( x=tumor, y=expr, col=tumor, shape=processing ) )

pheatmap( 
  pept83_vsn[ rownames( pept83_vsn )%in%pectin, order( mf_83$tumor ) ], colorRampPalette(brewer.pal( 11,"PuOr" ) )(100), cluster_rows = FALSE, cluster_cols = FALSE, na_col = "grey" , annotation_col = tumor )
```

Beta-1,3-glucosyltransferase
```{r}
ggplot( cbind( mf_83, expr=pept83_vsn[rownames(pept83_vsn)==54363,] ) )+geom_beeswarm( aes( x=tumor, y=expr, col=tumor, shape=processing ) )
peptides_83$`Leading razor protein`[54363]

gtransferase <- peptides_83_narm[ peptides_83_narm$`Leading razor protein` == "Q6Y288",1]
gtransferase <- as.integer( unlist(gtransferase ) )

pheatmap( 
  pept83_vsn[ rownames( pept83_vsn )%in%gtransferase, order( mf_83$tumor ) ], colorRampPalette(brewer.pal( 11,"PuOr" ) )(100), cluster_rows = FALSE, cluster_cols = FALSE, na_col = "grey", annotation_col = tumor )
```

Protein S100-B
```{r}
peptides_83$`Leading razor protein`[5035]
ggplot( cbind( mf_83, expr=pept83_vsn[rownames(pept83_vsn)==5035,] ) )+geom_beeswarm( aes( x=tumor, y=expr, col=tumor, shape=processing ) )

gtransferase <- peptides_83_narm[ peptides_83_narm$`Leading razor protein` == "P04271",1]
gtransferase <- as.integer( unlist(gtransferase ) )

pheatmap( 
  pept83_vsn[ rownames( pept83_vsn )%in%gtransferase, order( mf_83$tumor ) ], colorRampPalette(brewer.pal( 11,"PuOr" ) )(100), cluster_rows = FALSE, cluster_cols = FALSE, na_col = "grey", annotation_col = tumor )

```

Gamma-interferon-inducible lysosomal thiol reductase
```{r}
peptides_83$`Leading razor protein`[25517]
ggplot( cbind( mf_83, expr=pept83_vsn[rownames(pept83_vsn)==25517,] ) ) + geom_beeswarm( aes( x=tumor, y=expr, col=tumor, shape=processing ) )

treductase <- peptides_83_narm[ peptides_83_narm$`Leading razor protein` == "P13284",1]
treductase <- as.integer( unlist(treductase ) )

pheatmap( 
  pept83_vsn[ rownames( pept83_vsn )%in%treductase, order( mf_83$tumor )], colorRampPalette(brewer.pal( 11,"PuOr" ) )(100), cluster_rows = FALSE, cluster_cols = FALSE, na_col = "grey", annotation_col = tumor )

```


Galectin-1
```{r}
peptides_83$`Leading razor protein`[61029]
ggplot( cbind( mf_83, expr=pept83_vsn[rownames(pept83_vsn)==61029,] ) ) + geom_beeswarm( aes( x=tumor, y=expr, col=tumor, shape=processing ) )

galectin <- peptides_83_narm[ peptides_83_narm$`Leading razor protein` == "P09382",1]
galectin <- as.integer( unlist( galectin ) )

pheatmap( 
  pept83_vsn[ rownames( pept83_vsn )%in%galectin, order( mf_83$tumor ) ], colorRampPalette(brewer.pal( 11,"PuOr" ) )(100), cluster_rows = FALSE, cluster_cols = FALSE, na_col = "grey", annotation_col = tumor )

```


Fructosamine-3-kinase
```{r}
peptides_83$`Leading razor protein`[42567]
ggplot( cbind( mf_83, expr=pept83_vsn[rownames(pept83_vsn)==42567,] ) ) + geom_beeswarm( aes( x=tumor, y=expr, col=tumor, shape=processing ) )

fkinase <- peptides_83_narm[ peptides_83_narm$`Leading razor protein` == "Q9H479",1]
fkinase <- as.integer( unlist( fkinase ) )

pheatmap( 
  pept83_vsn[ rownames( pept83_vsn )%in%fkinase, order(mf_83$tumor) ], colorRampPalette(brewer.pal( 11,"PuOr" ) )(100), cluster_rows = FALSE, cluster_cols = FALSE, na_col = "grey", annotation_col = tumor )

```

Selenium-binding protein 1
```{r}
peptides_83$`Leading razor protein`[47619]
ggplot( cbind( mf_83, expr=pept83_vsn[rownames(pept83_vsn)==47619,] ) ) + geom_beeswarm( aes( x=tumor, y=expr, col=tumor, shape=processing ) )

seleniumprot <- peptides_83_narm[ peptides_83_narm$`Leading razor protein` == "Q13228",1]
seleniumprot <- as.integer( unlist( seleniumprot ) )

pheatmap( 
  pept83_vsn[ rownames( pept83_vsn )%in%seleniumprot, order(mf_83$tumor) ], colorRampPalette(brewer.pal( 11,"PuOr" ) )(100), cluster_rows = FALSE, cluster_cols = FALSE, na_col = "grey", annotation_col = tumor )

```

Calcyphosin
```{r}
peptides_83$`Leading razor protein`[13763]
ggplot( cbind( mf_83, expr=pept83_vsn[rownames(pept83_vsn)==13763,] ) ) + geom_beeswarm( aes( x=tumor, y=expr, col=tumor, shape=processing ) )

calcyphosin <- peptides_83_narm[ peptides_83_narm$`Leading razor protein` == "Q13228",1]
calcyphosin <- as.integer( unlist( calcyphosin ) )

pheatmap( 
  pept83_vsn[ rownames( pept83_vsn )%in%calcyphosin, order(mf_83$tumor) ], colorRampPalette(brewer.pal( 11,"PuOr" ) )(100), cluster_rows = FALSE, cluster_cols = FALSE, na_col = "grey", annotation_col = tumor )

```


Thymosin beta-4;Hematopoietic system regulatory peptide
```{r}
peptides_83$`Leading razor protein`[60164]
ggplot( cbind( mf_83, expr=pept83_vsn[rownames(pept83_vsn)==60164,] ) ) + geom_beeswarm( aes( x=tumor, y=expr, col=tumor, shape=processing ) )

thymosinbeta <- peptides_83_narm[ peptides_83_narm$`Leading razor protein` == "P62328",1]
thymosinbeta <- as.integer( unlist( thymosinbeta ) )

pheatmap( 
  pept83_vsn[ rownames( pept83_vsn )%in%thymosinbeta, order(mf_83$tumor) ], colorRampPalette(brewer.pal( 11,"PuOr" ) )(100), cluster_rows = FALSE, cluster_cols = FALSE, na_col = "grey", annotation_col = tumor )

```


Phosphatidylethanolamine-binding protein 1;Hippocampal cholinergic neurostimulating peptide
```{r}
peptides_83$`Leading razor protein`[74265]
ggplot( cbind( mf_83, expr=pept83_vsn[rownames(pept83_vsn)==74265,] ) ) + geom_beeswarm( aes( x=tumor, y=expr, col=tumor, shape=processing ) )

phosphatprot <- peptides_83_narm[ peptides_83_narm$`Leading razor protein` == "P30086",1]
phosphatprot <- as.integer( unlist( phosphatprot ) )

pheatmap( 
  pept83_vsn[ rownames( pept83_vsn )%in%phosphatprot, order(mf_83$tumor) ], colorRampPalette(brewer.pal( 11,"PuOr" ) )(100), cluster_rows = FALSE, cluster_cols = FALSE, na_col = "grey", annotation_col = tumor )

```


```{r}
pheatmap( 
  pept83_vsn[ rownames( pept83_vsn )%in%pept_id[1:1000], order( mf_83$tumor ) ], colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = FALSE, cluster_cols = FALSE, na_col = "grey" , annotation_col = tumor)

pheatmap( 
  pept83_vsn[ rownames( pept83_vsn )%in%pept_id[1:1000], order( mf_83$tumor ) ], colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = FALSE, cluster_cols = TRUE, na_col = "grey" , annotation_col = tumor)
```


compute distance matrix and run tsne
```{r}
dist83 <- dist( t( pept83_vsn ) )
dist83_man <- dist( t( pept83_vsn ), method = "manhattan" )

tsne_83 <- tsne( dist83, perplexity = 30 )
tsne_83 <- as.tibble( tsne_83 ) %>%
  mutate( tag = mf_83$tag ) %>%
  left_join( mf_83 )

tsne_83_p40 <- tsne( dist83, perplexity = 40 )
tsne_83_p40 <- as.tibble( tsne_83_p40 ) %>%
  mutate( tag = mf_83$tag ) %>%
  left_join( mf_83 )

tsne_83_p50 <- tsne( dist83, perplexity = 50 )
tsne_83_p50 <- as.tibble( tsne_83_p50 ) %>%
  mutate( tag = mf_83$tag ) %>%
  left_join( mf_83 )

tsne_83_p20 <- tsne( dist83, perplexity = 20 )
tsne_83_p20 <- as.tibble( tsne_83_p20 ) %>%
  mutate( tag = mf_83$tag ) %>%
  left_join( mf_83 )

tsne_83_p15 <- tsne( dist83, perplexity = 15 )
tsne_83_p15 <- as.tibble( tsne_83_p15 ) %>%
  mutate( tag = mf_83$tag ) %>%
  left_join( mf_83 )

tsne_83m <- tsne( dist83_man )
tsne_83m <- as.tibble( tsne_83m ) %>%
  mutate( tag = mf_83$tag ) %>%
  left_join( mf_83 )

ggplot( tsne_83 ) +
  geom_point( aes( x=V1, y=V2, col=tumor ), size=3 ) +
  scale_colour_manual( name="",  values = colorRampPalette(brewer.pal( 8,"Accent" ))(8) )

ggplot( tsne_83m ) +
  geom_point( aes( x=V1, y=V2, col=tumor ), size=3 ) +
  scale_colour_manual( name="",  values = colorRampPalette(brewer.pal( 8,"Accent" ))(8) )

```

remove pfb samples and run dist, tsne and heatmap clustering again
```{r}
pept83_vsn_pfb <- pept83_vsn[,mf_83$tumor!="pfb"]

dist83_pfb <- dist( t( pept83_vsn_pfb ) )

tsne_83_pfb <- tsne( dist83_pfb, perplexity = 25 )
tsne_83_pfb <- as.tibble( tsne_83_pfb ) %>%
  mutate( tag = mf_83$tag[mf_83$tumor!="pfb"] ) %>%
  left_join( mf_83 )

ggplot( tsne_83_pfb ) +
  geom_point( aes( x=V1, y=V2, col=tumor ), size=3 ) +
  scale_colour_manual( name="",  values = colorRampPalette(brewer.pal( 8,"Accent" ))(8) )

```

redo the fit on the pfb removed vsn data
```{r}
mm_pfb <- model.matrix( ~ processing + tumor , mf_83[mf_83$tumor!="pfb",] )
fit_pfb <- lmFit( pept83_vsn_pfb, mm_pfb )

pept_id_pfb <- unlist( as.integer( rownames_to_column(topTable(eBayes(fit_pfb), coef = 3:7, number = Inf)[1:2000,])[,1] ) )

pheatmap( 
  pept83_vsn_pfb[ rownames( pept83_vsn_pfb )%in%pept_id_pfb[1:1000], order( mf_83$tumor[mf_83$tumor!="pfb"] ) ], colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = FALSE, cluster_cols = TRUE, na_col = "grey" , annotation_col = tumor)
```

redo the fit on the pfb removed quantile normalized data 
```{r}
pept83_quant_pfb <- pept83_quant_log[ , mf_83$tumor!="pfb" ]
mm_pfb <- model.matrix( ~ processing + tumor , mf_83[mf_83$tumor!="pfb",] )
fit_quant_pfb <- lmFit( pept83_quant_pfb, mm_pfb )

pept_id_quant_pfb <- unlist( as.integer( rownames_to_column(topTable(eBayes(fit_quant_pfb), coef = 3:7, number = Inf)[1:2000,])[,1] ) )

pheatmap( 
  pept83_quant_pfb[ rownames( pept83_quant_pfb )%in%pept_id_quant_pfb[1:1000], order( mf_83$tumor[mf_83$tumor!="pfb"] ) ], colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = FALSE, cluster_cols = TRUE, na_col = "grey" , annotation_col = tumor)
```

remove pfb samples and run dist, tsne and heatmap clustering again (for quantile normalized data)
```{r}
dist83_quant_pfb <- dist( t( pept83_quant_pfb ) )

tsne83_quant_pfb <- tsne( dist83_quant_pfb, perplexity = 25 )
tsne83_quant_pfb <- as.tibble( tsne83_quant_pfb ) %>%
  mutate( tag = mf_83$tag[mf_83$tumor!="pfb"] ) %>%
  left_join( mf_83 )

ggplot( tsne83_quant_pfb ) +
  geom_point( aes( x=V1, y=V2, col=tumor ), size=3 ) +
  scale_colour_manual( name="",  values = colorRampPalette(brewer.pal( 8,"Accent" ))(8) )

```

find peptides that differ most from one tumor group (with respect to the other groups)
```{r}
mean_relpos <- rowMeans( pept83_vsn[, mf_83$tumor=="relpos"], na.rm = TRUE )
mean_pept_relpos <- rowMeans( pept83_vsn[, mf_83$tumor!="relpos"], na.rm =TRUE )

dmean_relpos <- mean_pept_relpos - mean_relpos %>%
  as.data.frame()
rownames( dmean_relpos ) = rownames( pept83_vsn )
dmean_relpos <- rownames_to_column( dmean_relpos )
dmean_relpos <-  rename( dmean_relpos, mean_diff = . )

pept83_relpos_id <- order( desc( abs( dmean_relpos$mean_diff ) ) )[1:5000]
pept83_relpos <- peptides_83[dmean_relpos$rowname[pept83_relpos_id],]
write.table( unique( pept83_relpos$`Leading razor protein` ), "peptides_relpos", row.names=FALSE, quote=FALSE )  

write.table( unique( peptides_83$`Leading razor protein` ), "peptides83", row.names=FALSE, quote=FALSE )
```

heatmap of the distance matrix
```{r}
 pheatmap( dist83_pfb, colorRampPalette(brewer.pal( 11,"PuOr" ))(100), cluster_rows = TRUE, cluster_cols = TRUE, na_col = "grey" , annotation_col = tumor)
```

```{r}

```


